# AI Output Curation And Quality Control > Designs a quality-control and curation process for AI-generated content or decisions — the shift from 'creator' to 'curator': what gets checked, who checks it, and against what criteria before publication or use. - Skill: `pilot2service/ai-output-curation-and-quality-control` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/ai-output-curation-and-quality-control` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/ai-output-curation-and-quality-control/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/ai-output-curation-and-quality-control --- # AI Output Curation & Quality Control ## Purpose Designs a process for assessing, correcting, and approving AI-generated content or decisions before use — a role in which the human no longer produces content from scratch ("creator") but evaluates and steers the AI's output toward an on-brand, coherent, reliable result ("curator"). ## Anchored in research - A research report supplied by the user, "AI Business Designer in the Age of AI" (2026) — "curation and quality control: the shift from creator to curator," part of the modeling and design competency area. - The human-in/on/outside-the-loop model, see `../../../specialisation-packs/ai-native-startup-design/skills/closed-loop-process-and-human-oversight-design/SKILL.md` — this skill applies the same model specifically to content/output quality, rather than general process design. ## Method 1. Define what the AI produces (text, code, image, decision recommendation, classification) and what it's used for — an internal purpose or a customer-facing one? 2. Define quality criteria before the AI starts producing: what does "good" mean for this output (factual accuracy, on-brand fit, tone, correctness, coherence)? 3. Choose the level of checking using the human-in/on/outside-the-loop model (see `../../../specialisation-packs/ai-native-startup-design/skills/closed-loop-process-and-human-oversight-design/SKILL.md`): high-stakes/high-risk outputs are always checked before publication, lower-risk outputs can be monitored with spot checks. 4. Name the curator(s) — who owns quality, and what's their role: they no longer produce content from scratch, but assess, correct, and approve/reject the AI's output. 5. Build a checklist or rubric that the curator uses consistently — a subjective "feels right" doesn't scale. For a fuller specification of what "on-brand and correct" means in AI-behavioral terms — tone, precision, and how the AI should redirect rather than just fail when it hits a limit — see `../../../human-ai-collaboration-design/skills/ai-behavioral-specification-design/SKILL.md` and `../../../human-ai-collaboration-design/skills/ai-accuracy-guardrails-and-grounding-design/SKILL.md`. 6. Design a feedback loop: how the curator's corrections are fed back into the prompt or system so the same mistakes don't recur (closed-loop thinking). 7. Track quality over time: what share of AI outputs pass through without correction — this tells you whether the process is maturing toward less human oversight or not. For the full audit methodology behind this step — override-rate thresholds in both directions, override accuracy, and turning corrections into a structured feedback loop — see `../../../human-ai-collaboration-design/skills/hitl-override-metrics-and-feedback-audit/SKILL.md`. A pass-through rate alone can mislead the same way a raw override rate can: a very low correction rate can mean excellent AI output, or it can mean curators have started rubber-stamping. 8. **Reference case for the creator-to-curator shift at business-model scale, not just individual-output scale:** Shutterstock, when free and unlimited AI-generated images threatened its core licensing business, didn't try to compete as a creator of stock images against free generation — it repositioned the whole company around curation and governance instead: a six-year training-data agreement with OpenAI, a Contributor Fund compensating artists whose work trains the models, and a pitch to enterprise customers built around content-usage governance and legal safety rather than image supply alone (independently confirmed: a real, current six-year agreement and an active contributor-compensation program). This is the same shift this skill designs at the level of a single output-review process, applied instead at the level of an entire business repositioning around curation — worth using with a client who's asking "should we compete with AI or curate around it" at a strategic level, not just a process level. ## What this skill does NOT do - Doesn't assess the AI model's technical performance (e.g. accuracy/ recall metrics) — that's a technical/data-science task; this skill is a business quality-assurance process. - Doesn't remove the need for human oversight on high-risk outputs just because the process exists — the curation process complements, it doesn't replace, responsible-AI principles (see `../responsible-ai-and-governance-check/SKILL.md`). - Doesn't make the final approve/reject decision for you on an individual output. ## Refinement notes Areas to keep deepening with real practice: - your own rules of thumb for when spot-check oversight is enough vs. when 100% checking is needed - concrete templates (into `../../references/`, e.g. a curation rubric) - reference cases / your own examples of quality control for AI outputs - what this skill deliberately does *not* do (guardrails, common mistakes) — add to the list above Once this section is filled in and validated in practice, update the `maturity` field in `skills_index.json` to `draft`, `validated`, or `canonical` (see `../../../meta/maturity_levels.md`). **Don't add new fields to the frontmatter** — `name` and `description` are the only ones allowed (see `../../../meta/frontmatter_schema.md`). ## Continue from here - In this pack: `../responsible-ai-and-governance-check/SKILL.md` - Related skill in another pack: `../../../specialisation-packs/ai-native-startup-design/skills/closed-loop-process-and-human-oversight-design/SKILL.md`, `../../../change-and-communication/skills/workshop-and-facilitation-design/SKILL.md` (training the curator team). - **For deeper operational governance once curation is live**: `../../../human-ai-collaboration-design/skills/hitl-override-metrics-and-feedback-audit/SKILL.md` (deepens step 7), `../../../human-ai-collaboration-design/skills/ai-behavioral-specification-design/SKILL.md` and `../../../human-ai-collaboration-design/skills/ai-accuracy-guardrails-and-grounding-design/SKILL.md` (deepen step 5's rubric). - A ready-made skill chain for this situation: see `../../../playbooks/` - This pack's shared guardrails: `../../CLAUDE.md` ## References - `../../references/ai-native-reshuffle-heuristics-research.md` — grounding for the Shutterstock reference case in Method step 8 - `../../references/` — the pack's shared background material - `../../CLAUDE.md` — the pack's shared guardrails